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Record W2773860690 · doi:10.1080/17508975.2017.1394810

Intelligent or smart cities and buildings: a critical exposition and a way forward

2017· article· en· W2773860690 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIntelligent Buildings International · 2017
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsContext (archaeology)Building automationExposition (narrative)Architectural engineeringField (mathematics)PreferenceCorporate governanceSmart cityFacility managementComputer scienceEngineeringKnowledge managementBusinessComputer securityInternet of ThingsMarketing

Abstract

fetched live from OpenAlex

In the last decade, there has been an undoubtedly rising interest in the field of intelligent and smart built environments from design and construction to management, operational and governance perspectives. These recent endeavors, observed at both academic and professional levels, can be classified into city, neighborhood and building scales. In this context, understanding what we really mean by the word intelligent and smart is crucially important. This technical note attempts to clarify and further explore how intelligence differs from smartness in this context. Having intelligence as the main umbrella embracing other interrelated smart subsets is one way of thinking as supported by previous debates, while there are also other lines of thinking with more preference on the smartness as the core concept.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.281
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it